As data volumes grow and real-time processing becomes essential, traditional cloud architectures face limitations in cost, latency, and security. The traditional approach moves all edge data to where the queries are executed—in the cloud—leading to inefficiencies and high costs. EdgeLake (https://lfedge.org/projects/edgelake/), an LF Edge project, takes the opposite approach by bringing queries to the source data at the edge, enabling decentralized data management and local AI/ML processing.

In this talk, we’ll explore how EdgeLake eliminates cloud dependencies, optimizes data infrastructure, and reduces operational costs while ensuring real-time decision-making at the edge. We’ll discuss key use cases (and show a live demo) across industrial automation, smart cities, energy, and telecom, demonstrating how organizations can leverage EdgeLake to unlock the full potential of edge computing.

Join us to learn how EdgeLake is reshaping the future of distributed data architectures and making edge intelligence more accessible.